Introdução ao R para as Ciências humanas: visualização e manipulação de dados
R-Ladies Natal
Mahayana Godoy
outubro/2020
Reprodução: permite reprodução da análise, dando espaço a auditoria e facilidade em colaboração
Versatilidade: mais possibilidades que uma planilha de excel
Visualização de dados com o ggplot
Manipulação e criação de tabelas com o dplyr
Ambos fazem parte do pacote tidyverse
Pacote gapminder para nosso conjunto de dados
O R é uma calculadora
## [1] 4
## [1] 8
## [1] 13
Ele também usa funções para seus cálculos
## [1] 9
## [1] 0.6931472
## [1] 148.4132
Assumindo que você já instalou o pacote com install.packages() conforme a mensagem que enviei antes do curso, vamos carregar os pacotes a serem usados.
Se você quiser aprender a importar seu próprio conjunto de dados, veja a seção 1 do material em mahayana.me/mlm
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rows
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
Você pode salvar o conjunto de dados com outro nome (ou o mesmo) na área Environment
Agora você pode clicar e ver esse conjunto de dados na sua área Environment.
## [1] Asia Asia Asia Asia Asia Asia Asia Asia
## [9] Asia Asia Asia Asia Europe Europe Europe Europe
## [17] Europe Europe Europe Europe Europe Europe Europe Europe
## [25] Africa Africa Africa Africa Africa Africa Africa Africa
## [33] Africa Africa Africa Africa Africa Africa Africa Africa
## [41] Africa Africa Africa Africa Africa Africa Africa Africa
## [49] Americas Americas Americas Americas Americas Americas Americas Americas
## [57] Americas Americas Americas Americas Oceania Oceania Oceania Oceania
## [65] Oceania Oceania Oceania Oceania Oceania Oceania Oceania Oceania
## [73] Europe Europe Europe Europe Europe Europe Europe Europe
## [81] Europe Europe Europe Europe Asia Asia Asia Asia
## [89] Asia Asia Asia Asia Asia Asia Asia Asia
## [97] Asia Asia Asia Asia Asia Asia Asia Asia
## [105] Asia Asia Asia Asia Europe Europe Europe Europe
## [113] Europe Europe Europe Europe Europe Europe Europe Europe
## [121] Africa Africa Africa Africa Africa Africa Africa Africa
## [129] Africa Africa Africa Africa Americas Americas Americas Americas
## [137] Americas Americas Americas Americas Americas Americas Americas Americas
## [145] Europe Europe Europe Europe Europe Europe Europe Europe
## [153] Europe Europe Europe Europe Africa Africa Africa Africa
## [161] Africa Africa Africa Africa Africa Africa Africa Africa
## [169] Americas Americas Americas Americas Americas Americas Americas Americas
## [177] Americas Americas Americas Americas Europe Europe Europe Europe
## [185] Europe Europe Europe Europe Europe Europe Europe Europe
## [193] Africa Africa Africa Africa Africa Africa Africa Africa
## [201] Africa Africa Africa Africa Africa Africa Africa Africa
## [209] Africa Africa Africa Africa Africa Africa Africa Africa
## [217] Asia Asia Asia Asia Asia Asia Asia Asia
## [225] Asia Asia Asia Asia Africa Africa Africa Africa
## [233] Africa Africa Africa Africa Africa Africa Africa Africa
## [241] Americas Americas Americas Americas Americas Americas Americas Americas
## [249] Americas Americas Americas Americas Africa Africa Africa Africa
## [257] Africa Africa Africa Africa Africa Africa Africa Africa
## [265] Africa Africa Africa Africa Africa Africa Africa Africa
## [273] Africa Africa Africa Africa Americas Americas Americas Americas
## [281] Americas Americas Americas Americas Americas Americas Americas Americas
## [289] Asia Asia Asia Asia Asia Asia Asia Asia
## [297] Asia Asia Asia Asia Americas Americas Americas Americas
## [305] Americas Americas Americas Americas Americas Americas Americas Americas
## [313] Africa Africa Africa Africa Africa Africa Africa Africa
## [321] Africa Africa Africa Africa Africa Africa Africa Africa
## [329] Africa Africa Africa Africa Africa Africa Africa Africa
## [337] Africa Africa Africa Africa Africa Africa Africa Africa
## [345] Africa Africa Africa Africa Americas Americas Americas Americas
## [353] Americas Americas Americas Americas Americas Americas Americas Americas
## [361] Africa Africa Africa Africa Africa Africa Africa Africa
## [369] Africa Africa Africa Africa Europe Europe Europe Europe
## [377] Europe Europe Europe Europe Europe Europe Europe Europe
## [385] Americas Americas Americas Americas Americas Americas Americas Americas
## [393] Americas Americas Americas Americas Europe Europe Europe Europe
## [401] Europe Europe Europe Europe Europe Europe Europe Europe
## [409] Europe Europe Europe Europe Europe Europe Europe Europe
## [417] Europe Europe Europe Europe Africa Africa Africa Africa
## [425] Africa Africa Africa Africa Africa Africa Africa Africa
## [433] Americas Americas Americas Americas Americas Americas Americas Americas
## [441] Americas Americas Americas Americas Americas Americas Americas Americas
## [449] Americas Americas Americas Americas Americas Americas Americas Americas
## [457] Africa Africa Africa Africa Africa Africa Africa Africa
## [465] Africa Africa Africa Africa Americas Americas Americas Americas
## [473] Americas Americas Americas Americas Americas Americas Americas Americas
## [481] Africa Africa Africa Africa Africa Africa Africa Africa
## [489] Africa Africa Africa Africa Africa Africa Africa Africa
## [497] Africa Africa Africa Africa Africa Africa Africa Africa
## [505] Africa Africa Africa Africa Africa Africa Africa Africa
## [513] Africa Africa Africa Africa Europe Europe Europe Europe
## [521] Europe Europe Europe Europe Europe Europe Europe Europe
## [529] Europe Europe Europe Europe Europe Europe Europe Europe
## [537] Europe Europe Europe Europe Africa Africa Africa Africa
## [545] Africa Africa Africa Africa Africa Africa Africa Africa
## [553] Africa Africa Africa Africa Africa Africa Africa Africa
## [561] Africa Africa Africa Africa Europe Europe Europe Europe
## [569] Europe Europe Europe Europe Europe Europe Europe Europe
## [577] Africa Africa Africa Africa Africa Africa Africa Africa
## [585] Africa Africa Africa Africa Europe Europe Europe Europe
## [593] Europe Europe Europe Europe Europe Europe Europe Europe
## [601] Americas Americas Americas Americas Americas Americas Americas Americas
## [609] Americas Americas Americas Americas Africa Africa Africa Africa
## [617] Africa Africa Africa Africa Africa Africa Africa Africa
## [625] Africa Africa Africa Africa Africa Africa Africa Africa
## [633] Africa Africa Africa Africa Americas Americas Americas Americas
## [641] Americas Americas Americas Americas Americas Americas Americas Americas
## [649] Americas Americas Americas Americas Americas Americas Americas Americas
## [657] Americas Americas Americas Americas Asia Asia Asia Asia
## [665] Asia Asia Asia Asia Asia Asia Asia Asia
## [673] Europe Europe Europe Europe Europe Europe Europe Europe
## [681] Europe Europe Europe Europe Europe Europe Europe Europe
## [689] Europe Europe Europe Europe Europe Europe Europe Europe
## [697] Asia Asia Asia Asia Asia Asia Asia Asia
## [705] Asia Asia Asia Asia Asia Asia Asia Asia
## [713] Asia Asia Asia Asia Asia Asia Asia Asia
## [721] Asia Asia Asia Asia Asia Asia Asia Asia
## [729] Asia Asia Asia Asia Asia Asia Asia Asia
## [737] Asia Asia Asia Asia Asia Asia Asia Asia
## [745] Europe Europe Europe Europe Europe Europe Europe Europe
## [753] Europe Europe Europe Europe Asia Asia Asia Asia
## [761] Asia Asia Asia Asia Asia Asia Asia Asia
## [769] Europe Europe Europe Europe Europe Europe Europe Europe
## [777] Europe Europe Europe Europe Americas Americas Americas Americas
## [785] Americas Americas Americas Americas Americas Americas Americas Americas
## [793] Asia Asia Asia Asia Asia Asia Asia Asia
## [801] Asia Asia Asia Asia Asia Asia Asia Asia
## [809] Asia Asia Asia Asia Asia Asia Asia Asia
## [817] Africa Africa Africa Africa Africa Africa Africa Africa
## [825] Africa Africa Africa Africa Asia Asia Asia Asia
## [833] Asia Asia Asia Asia Asia Asia Asia Asia
## [841] Asia Asia Asia Asia Asia Asia Asia Asia
## [849] Asia Asia Asia Asia Asia Asia Asia Asia
## [857] Asia Asia Asia Asia Asia Asia Asia Asia
## [865] Asia Asia Asia Asia Asia Asia Asia Asia
## [873] Asia Asia Asia Asia Africa Africa Africa Africa
## [881] Africa Africa Africa Africa Africa Africa Africa Africa
## [889] Africa Africa Africa Africa Africa Africa Africa Africa
## [897] Africa Africa Africa Africa Africa Africa Africa Africa
## [905] Africa Africa Africa Africa Africa Africa Africa Africa
## [913] Africa Africa Africa Africa Africa Africa Africa Africa
## [921] Africa Africa Africa Africa Africa Africa Africa Africa
## [929] Africa Africa Africa Africa Africa Africa Africa Africa
## [937] Asia Asia Asia Asia Asia Asia Asia Asia
## [945] Asia Asia Asia Asia Africa Africa Africa Africa
## [953] Africa Africa Africa Africa Africa Africa Africa Africa
## [961] Africa Africa Africa Africa Africa Africa Africa Africa
## [969] Africa Africa Africa Africa Africa Africa Africa Africa
## [977] Africa Africa Africa Africa Africa Africa Africa Africa
## [985] Americas Americas Americas Americas Americas Americas Americas Americas
## [993] Americas Americas Americas Americas Asia Asia Asia Asia
## [1001] Asia Asia Asia Asia Asia Asia Asia Asia
## [1009] Europe Europe Europe Europe Europe Europe Europe Europe
## [1017] Europe Europe Europe Europe Africa Africa Africa Africa
## [1025] Africa Africa Africa Africa Africa Africa Africa Africa
## [1033] Africa Africa Africa Africa Africa Africa Africa Africa
## [1041] Africa Africa Africa Africa Asia Asia Asia Asia
## [1049] Asia Asia Asia Asia Asia Asia Asia Asia
## [1057] Africa Africa Africa Africa Africa Africa Africa Africa
## [1065] Africa Africa Africa Africa Asia Asia Asia Asia
## [1073] Asia Asia Asia Asia Asia Asia Asia Asia
## [1081] Europe Europe Europe Europe Europe Europe Europe Europe
## [1089] Europe Europe Europe Europe Oceania Oceania Oceania Oceania
## [1097] Oceania Oceania Oceania Oceania Oceania Oceania Oceania Oceania
## [1105] Americas Americas Americas Americas Americas Americas Americas Americas
## [1113] Americas Americas Americas Americas Africa Africa Africa Africa
## [1121] Africa Africa Africa Africa Africa Africa Africa Africa
## [1129] Africa Africa Africa Africa Africa Africa Africa Africa
## [1137] Africa Africa Africa Africa Europe Europe Europe Europe
## [1145] Europe Europe Europe Europe Europe Europe Europe Europe
## [1153] Asia Asia Asia Asia Asia Asia Asia Asia
## [1161] Asia Asia Asia Asia Asia Asia Asia Asia
## [1169] Asia Asia Asia Asia Asia Asia Asia Asia
## [1177] Americas Americas Americas Americas Americas Americas Americas Americas
## [1185] Americas Americas Americas Americas Americas Americas Americas Americas
## [1193] Americas Americas Americas Americas Americas Americas Americas Americas
## [1201] Americas Americas Americas Americas Americas Americas Americas Americas
## [1209] Americas Americas Americas Americas Asia Asia Asia Asia
## [1217] Asia Asia Asia Asia Asia Asia Asia Asia
## [1225] Europe Europe Europe Europe Europe Europe Europe Europe
## [1233] Europe Europe Europe Europe Europe Europe Europe Europe
## [1241] Europe Europe Europe Europe Europe Europe Europe Europe
## [1249] Americas Americas Americas Americas Americas Americas Americas Americas
## [1257] Americas Americas Americas Americas Africa Africa Africa Africa
## [1265] Africa Africa Africa Africa Africa Africa Africa Africa
## [1273] Europe Europe Europe Europe Europe Europe Europe Europe
## [1281] Europe Europe Europe Europe Africa Africa Africa Africa
## [1289] Africa Africa Africa Africa Africa Africa Africa Africa
## [1297] Africa Africa Africa Africa Africa Africa Africa Africa
## [1305] Africa Africa Africa Africa Asia Asia Asia Asia
## [1313] Asia Asia Asia Asia Asia Asia Asia Asia
## [1321] Africa Africa Africa Africa Africa Africa Africa Africa
## [1329] Africa Africa Africa Africa Europe Europe Europe Europe
## [1337] Europe Europe Europe Europe Europe Europe Europe Europe
## [1345] Africa Africa Africa Africa Africa Africa Africa Africa
## [1353] Africa Africa Africa Africa Asia Asia Asia Asia
## [1361] Asia Asia Asia Asia Asia Asia Asia Asia
## [1369] Europe Europe Europe Europe Europe Europe Europe Europe
## [1377] Europe Europe Europe Europe Europe Europe Europe Europe
## [1385] Europe Europe Europe Europe Europe Europe Europe Europe
## [1393] Africa Africa Africa Africa Africa Africa Africa Africa
## [1401] Africa Africa Africa Africa Africa Africa Africa Africa
## [1409] Africa Africa Africa Africa Africa Africa Africa Africa
## [1417] Europe Europe Europe Europe Europe Europe Europe Europe
## [1425] Europe Europe Europe Europe Asia Asia Asia Asia
## [1433] Asia Asia Asia Asia Asia Asia Asia Asia
## [1441] Africa Africa Africa Africa Africa Africa Africa Africa
## [1449] Africa Africa Africa Africa Africa Africa Africa Africa
## [1457] Africa Africa Africa Africa Africa Africa Africa Africa
## [1465] Europe Europe Europe Europe Europe Europe Europe Europe
## [1473] Europe Europe Europe Europe Europe Europe Europe Europe
## [1481] Europe Europe Europe Europe Europe Europe Europe Europe
## [1489] Asia Asia Asia Asia Asia Asia Asia Asia
## [1497] Asia Asia Asia Asia Asia Asia Asia Asia
## [1505] Asia Asia Asia Asia Asia Asia Asia Asia
## [1513] Africa Africa Africa Africa Africa Africa Africa Africa
## [1521] Africa Africa Africa Africa Asia Asia Asia Asia
## [1529] Asia Asia Asia Asia Asia Asia Asia Asia
## [1537] Africa Africa Africa Africa Africa Africa Africa Africa
## [1545] Africa Africa Africa Africa Americas Americas Americas Americas
## [1553] Americas Americas Americas Americas Americas Americas Americas Americas
## [1561] Africa Africa Africa Africa Africa Africa Africa Africa
## [1569] Africa Africa Africa Africa Europe Europe Europe Europe
## [1577] Europe Europe Europe Europe Europe Europe Europe Europe
## [1585] Africa Africa Africa Africa Africa Africa Africa Africa
## [1593] Africa Africa Africa Africa Europe Europe Europe Europe
## [1601] Europe Europe Europe Europe Europe Europe Europe Europe
## [1609] Americas Americas Americas Americas Americas Americas Americas Americas
## [1617] Americas Americas Americas Americas Americas Americas Americas Americas
## [1625] Americas Americas Americas Americas Americas Americas Americas Americas
## [1633] Americas Americas Americas Americas Americas Americas Americas Americas
## [1641] Americas Americas Americas Americas Asia Asia Asia Asia
## [1649] Asia Asia Asia Asia Asia Asia Asia Asia
## [1657] Asia Asia Asia Asia Asia Asia Asia Asia
## [1665] Asia Asia Asia Asia Asia Asia Asia Asia
## [1673] Asia Asia Asia Asia Asia Asia Asia Asia
## [1681] Africa Africa Africa Africa Africa Africa Africa Africa
## [1689] Africa Africa Africa Africa Africa Africa Africa Africa
## [1697] Africa Africa Africa Africa Africa Africa Africa Africa
## Levels: Africa Americas Asia Europe Oceania
## [1] Asia Europe Africa Americas Oceania
## Levels: Africa Americas Asia Europe Oceania
## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
##
ggplot2: pacote de visualização gráfica de dados
Baseado no livro Grammar of Graphics (Leland Wilkinson)
Um gráfico é como se fosse uma frase de uma lÃngua, e cada elemento do gráfico é uma palavra
dados: o conjunto de dados a ser utilizado para trabalhar a visualização
aes: estética (aesthetics), indicando as variáveis selecionadas para plotagem, agrupamento, coloração etc.
Como visualizar a relação entre PIB per capta e expectativa de vida?
dados: o conjunto de dados a ser utilizado para trabalhar a visualização
aes: estética (aesthetics), indicando as variáveis selecionadas para plotagem, agrupamento, coloração etc.
geom: geometria, determinando o tipo gráfico a ser usado (pontos, linhas, barras, colunas etc.)
Problemas: dados de todos os anos!
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Queremos selecionar apenas dados em que year é igual a 2007
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rows
## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.
## 7 Austria Europe 2007 79.8 8199783 36126.
## 8 Bahrain Asia 2007 75.6 708573 29796.
## 9 Bangladesh Asia 2007 64.1 150448339 1391.
## 10 Belgium Europe 2007 79.4 10392226 33693.
## # … with 132 more rows
## # A tibble: 12 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Brazil Americas 1952 50.9 56602560 2109.
## 2 Brazil Americas 1957 53.3 65551171 2487.
## 3 Brazil Americas 1962 55.7 76039390 3337.
## 4 Brazil Americas 1967 57.6 88049823 3430.
## 5 Brazil Americas 1972 59.5 100840058 4986.
## 6 Brazil Americas 1977 61.5 114313951 6660.
## 7 Brazil Americas 1982 63.3 128962939 7031.
## 8 Brazil Americas 1987 65.2 142938076 7807.
## 9 Brazil Americas 1992 67.1 155975974 6950.
## 10 Brazil Americas 1997 69.4 168546719 7958.
## 11 Brazil Americas 2002 71.0 179914212 8131.
## 12 Brazil Americas 2007 72.4 190010647 9066.
Se eu quiser, posso salvar esse subconjunto naminha area Environment
dplyr%>% ou pipe: leva o resultado do código de uma linha e leva como input da linha seguinteselect (seleciona colunas) e filter (filtra por observações das linhas)CTRL + SHIFT + M# combinando select e filter
gapminder %>%
select(gdpPercap, lifeExp, year) %>%
filter(year == 2007)## # A tibble: 142 x 3
## gdpPercap lifeExp year
## <dbl> <dbl> <int>
## 1 975. 43.8 2007
## 2 5937. 76.4 2007
## 3 6223. 72.3 2007
## 4 4797. 42.7 2007
## 5 12779. 75.3 2007
## 6 34435. 81.2 2007
## 7 36126. 79.8 2007
## 8 29796. 75.6 2007
## 9 1391. 64.1 2007
## 10 33693. 79.4 2007
## # … with 132 more rows
! ERRO
Por quê?
# grafico basico
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point()Que outras informações seriam interessantes?
# mais informacoes
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(shape = continent))# mais informacoes 2
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent))# tema bw
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent))+
theme_bw()# tema classic
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent))+
theme_classic()Veja ?theme para mais informações sobre o tema
# ajustanto pontos: alpha
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent), alpha = 0.5)+
theme_bw()# ajustando pontos: shape
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent), shape = 2)+
theme_bw()# ajustando pontos: size
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent, size = pop), shape = 2)+
theme_bw()# informacao
gapminder %>%
filter(year == 2007) %>%
ggplot(., aes(gdpPercap, lifeExp))+
geom_point(aes(color = continent), alpha = 0.5)+
theme_bw()+
labs(title = "Relação entre PIB per capta e Expectativa de vida em 2007", x = "PIB per capta", y = "Expectativa de vida (em anos)")Reproduza o gráfico abaixo
Que gráfico teremos a partir desse código?
Esse código está errado. Como corrigir?
gapminder %>%
ggplot(., aes(year, lifeExp))+
geom_point(alpha = 0.5, aes(color = continent))+
theme_bw()# usando facet_wrap
gapminder %>%
ggplot(., aes(year, lifeExp))+
geom_point(alpha = 0.5, aes(color = continent))+
theme_bw()+
facet_wrap(~ continent)
E se eu quiser excluir a Oceania?!
Como resolver?
gapminder %>%
filter(continent == "Africa" | continent == "Americas" | continent == "Asia" | continent == "Europe") %>%
ggplot(., aes(year, lifeExp))+
geom_point(alpha = 0.5, aes(color = continent))+
theme_bw()+
facet_wrap(~ continent)ou
# excluindo a Oceania
gapminder %>%
filter(continent != "Oceania") %>%
ggplot(., aes(year, lifeExp))+
geom_point(alpha = 0.5, aes(color = continent))+
theme_bw()+
facet_wrap(~ continent)# excluindo a Oceania, 4 colunas
gapminder %>%
filter(continent != "Oceania") %>%
ggplot(., aes(year, lifeExp))+
geom_point(alpha = 0.5, aes(color = continent))+
theme_bw()+
facet_wrap(~ continent, ncol = 4)Como se constrói esse gráfico? O que vai no eixo x e o que vai no eixo y?
Código base, mas não temos a média!
Tabela que precisamos
## # A tibble: 60 x 3
## # Groups: year [12]
## year continent media
## <int> <fct> <dbl>
## 1 1952 Africa 39.1
## 2 1952 Americas 53.3
## 3 1952 Asia 46.3
## 4 1952 Europe 64.4
## 5 1952 Oceania 69.3
## 6 1957 Africa 41.3
## 7 1957 Americas 56.0
## 8 1957 Asia 49.3
## 9 1957 Europe 66.7
## 10 1957 Oceania 70.3
## # … with 50 more rows
Novas funções do dplyr:
group_by: agrupa observações pelas variáveis informadas
summarise: cria um novo dataframe a partir das variáveis agrupadas
# novo conjunto de dados
gapminder %>%
group_by(year, continent) %>%
summarise(media = mean(lifeExp))## # A tibble: 60 x 3
## # Groups: year [12]
## year continent media
## <int> <fct> <dbl>
## 1 1952 Africa 39.1
## 2 1952 Americas 53.3
## 3 1952 Asia 46.3
## 4 1952 Europe 64.4
## 5 1952 Oceania 69.3
## 6 1957 Africa 41.3
## 7 1957 Americas 56.0
## 8 1957 Asia 49.3
## 9 1957 Europe 66.7
## 10 1957 Oceania 70.3
## # … with 50 more rows
# novo conjunto de dados: apenas ano
gapminder %>%
group_by(year) %>%
summarise(media = mean(lifeExp))## # A tibble: 12 x 2
## year media
## <int> <dbl>
## 1 1952 49.1
## 2 1957 51.5
## 3 1962 53.6
## 4 1967 55.7
## 5 1972 57.6
## 6 1977 59.6
## 7 1982 61.5
## 8 1987 63.2
## 9 1992 64.2
## 10 1997 65.0
## 11 2002 65.7
## 12 2007 67.0
# novo conjunto de dados: apenas pais
gapminder %>%
group_by(continent) %>%
summarise(media = mean(lifeExp))## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 2
## continent media
## <fct> <dbl>
## 1 Africa 48.9
## 2 Americas 64.7
## 3 Asia 60.1
## 4 Europe 71.9
## 5 Oceania 74.3
# combinando informacoes
gapminder %>%
group_by(year, continent) %>%
summarise(media = mean(lifeExp)) %>%
ggplot(., aes(year, media))+
geom_line(aes(color = continent))+
theme_bw()Faça um gráfico mostrando o crescimento do pib per capta médio dos continentes europeu, africano e americano.
gapminder %>%
filter(continent == "Europe" | continent == "Africa" | continent == "Americas") %>%
group_by(year, continent) %>%
summarise(media = mean(lifeExp)) %>%
ggplot(., aes(year, media))+
geom_line(aes(color = continent))+
theme_bw()Quero fazer outros gráficos ou análises do meu material. Como fazer isso?
stackoverflow br: https://pt.stackoverflow.com/questions/tagged/r
stackoverflow: https://stackoverflow.com/questions/tagged/r
Obrigada!
contato: mahayanag@gmail.com